AWS Machine Learning and AI

Accelerate innovation cycles with a comprehensive set of AI and ML services in the AWS cloud.

Leveraging the Suite of Machine Learning Capabilities on AWS

Machine learning is revolutionizing the way businesses operate by automating complex processes and solving previously intractable problems. However, building and productionizing AI-based systems can pose several challenges that often leave machine learning initiatives stuck in the prototyping stage, frequently due to extensive infrastructure and scalability requirements or lack of technical expertise. AWS alleviates many of these challenges through pre-built services that abstract away many of the complex underlying technical details, allowing users to focus on the data and models.

Adastra’s AI services enable you to fully leverage the suite of machine learning capabilities on AWS to both build and deploy production-grade machine learning models in the cloud. We can assess your organization’s analytics goals and design an environment that supports end-to-end AI/ML development, from data preparation, model development, training/tuning, deployment, and management. Our team of data science experts can build a wide range of models to support your business-specific use cases and establish frameworks and best practices for future development.

Why Embrace AWS Machine Learning and Artificial Intelligence? 

Efficiency and Productivity

Automate complex tasks, streamline data processing, improve resource utilization, and focus efforts on future innovation.

Enhanced Customer Experience

Personalize customer interaction and enable real-time insights into customer behavior.

Competitive Advantage

Stay ahead of the curve by embracing new technologies and innovation cycles.

Increased Profitability

Enable better decision-making by providing insights into sales trends, customer preferences, and market conditions.

About the whitepaper

Driving Business Value with Generative AI on AWS

For over 20 years, Adastra has worked in the data and analytics space, developing custom artificial intelligence (AI)-based solutions for clients across different sectors and applications.

In cooperation with AWS and their array of innovative AI and ML solutions, Adastra leverages new advances in technology to accelerate processes. By moving quickly from evaluation to implementation, Adastra delivers innovative and practical generative AI-based solutions for clients and their customers.

Generative AI’s superpower is its ability to create new content. By analyzing patterns and features from historical data, it can be trained to create new content such as images, audio and text based on that harvested knowledge.

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AWS Machine Learning and AI Solution Areas

AI and Machine Learning

Develop custom and robust AI/ML models for various business needs.

Statistical Analysis

Perform deeper dives on insights and ensure statistically valid conclusions.

Natural Language Processing

Extend and analyze data from various semi-structured and unstructured formats.

Computer Cognition 

Leverage signal processing and deep learning to capture deeper insights into image, audio, or video data.

Forecasting and Optimization

Use predicted insights as well as optimization modelling to determine the best actionable approaches to business problems.

MLOps

Apply MLOps best practices to bring machine learning workflows to production and reduce bottlenecks.

Adastra’s Approach to AWS Machine Learning and AI

Conduct a series of discovery sessions with relevant stakeholders to gain a thorough understanding of your machine learning and AI goals, aligned to specific business outcomes and data assets.

Establish a SageMaker environment to enable data preparation and development of AI/ML models, with connectivity to key data stores.

Iteratively prototype models to obtain desired accuracy, including feature augmentation, feature engineering, and model development cycles. Incorporate pre-built AWS ML services depending on the specific use case. Leverage SageMaker Model Registry to catalog and manage model versions.

Leverage SageMaker endpoints to deploy machine learning (ML) models for batches of real-time inference. 

Measure model performance and quickly identify when issues arise, such as model degradation or data drift. Automatically and continuously retrain models to ensure the most accurate predictions.

Establish patterns and best practices for model development and production to enable faster time-to-market for new use cases. 

Success Stories

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